Muhammad Hasan Ferdous

I am a Ph.D. candidate in Information Systems at UMBC specializing in causal AI, temporal causal discovery, and robust analysis of complex multivariate time series. My research focuses on developing methods that remain reliable under autocorrelation, non-stationarity, latent structure, and irregular sampling, with applications in healthcare, climate analytics, and cybersecurity.

I have contributed several frameworks to the field, including CDANs, eCDANs, DCD (Decomposition-based Causal Discovery), and TimeGraph, a synthetic benchmark suite that evaluates causal discovery algorithms under realistic temporal challenges. My work aims to bridge theory and practice by producing interpretable, intervention-relevant causal models that support high-stakes decision systems.

As a Graduate Teaching Assistant at UMBC, I have supported courses such as Structured Systems Analysis and Design, Database Program Development, Advanced Database Project, and Management Information Systems. I emphasize hands-on learning, analytical thinking, and accessible instruction that prepares students for pathways in AI/ML, data science, and business analytics.

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Muhammad Hasan Ferdous

Example Talk

DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data

Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shift

TimeGraph: Synthetic Benchmark Datasets for Robust Time-Series Causal Discovery

Correlation to Causation: A Causal Deep Learning Framework for Arctic Sea Ice Prediction

Why Large Language Models Fail When the World Changes And Why Causality Is No Longer Optional

Beyond Next-Token Prediction: Why Agentic AI Needs Causal Guardrails

CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Data

eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-Stationary Data (Student Abstract)